Toward a Smart Tourism Recommender System: Applied to Tangier City

Aarab, Zineb; Elghazi, Asmae; Saidi, Rajaa; Rahmani, Moulay Driss

doi:10.1007/978-3-319-74500-8_59

Cited by 7 publications

(9 citation statements)

References 10 publications

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“…Moreover, as shown by the diagram of Fig. 2, 74% of the studies (i.e., 20 studies [89][90][91][92][93][94][95][96][97][98][99][100][101][102][103][104][105][106][107][108]) were published since 2017 and there is a trend towards an increasing number of publications over the years.…”

Section: Demographics Of the Studiesmentioning

confidence: 99%

“…[97] An audio augmented reality application for providing informative tourism services and engaging experiences [98] An application to provide tourism recommendations [99] A pollution monitoring and alert application that proactively notifies citizens about air quality [100] Intelligent traffic management application [101] A parking application to assist drivers in finding parking slots dynamically while moving or arriving at the destination [102] An application able to recommend contents and services according to context and users' profile [103] An application to give daily proactive recommendations for appropriately rescheduling the waste collection day in presence of problematic weather events [104] An application for promoting physical activity among older adults [105] An application to monitor air quality [106] A recommender application for smart markets [107] An application to provide a smart environment that can be adapted according to the user's agenda and the contextual information retrieved from mobile devices [108] An application to provide individual air quality indexes location-based components, social-aware components, or ontologies). Moreover, in general, the requirements of potential target users were not reported.…”

Section: Characteristics Of the Context-aware Applications Supported ...mentioning

confidence: 99%

“…5 Percentage of the studies reporting the use of the personal sensors, sensors deployed in the city, and both personal sensors and sensors deployed in the city To identify safety incidents [86] To collect and share data related to the environmental conditions of the city [90] To conduct participatory sensing campaigns [93] To determine the quality of the city routes [93,104] of IoT, although just two studies referred the protocols being used. Despite in some cases it is possible to customize the applications (i.e., personalization [31]), through the insertion of data (e.g., user profiles [84,95,98], preferences [93,94,104], health conditions [104], fixed location or fixed activity schedule [95]), all the applications of the included studies do not allow the execution of actions without the explicit consent of the user (i.e., they are passive applications [31]), which means they present suggestions, and it is up to the users to accept or not those suggestions.…”

Section: Characteristics Of the Context-aware Technologies Being Usedmentioning

confidence: 99%

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Systematic literature review of context-awareness applications supported by smart cities’ infrastructures

et al. 2022

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Since none of the existing systematic reviews of the literature related to smart cities have focused on smart cities applications using context-aware features, this study aims to provide a comprehensive view of (1) the characteristics of context-aware applications supported by smart cities’ infrastructures, (2) the characteristics of the context-aware technologies being used, and (3) the maturity level of the identified applications. An electronic search was conducted on Scopus, Web of Science, and IEEE Xplore databases, combining relevant keywords. After the studies’ selection, 27 studies were included. The results show the interest in using context-aware features to develop smart cities’ applications targeting public health, tourism experience, urban mobility, active citizenship, shopping experience, management of urban infrastructures, public alerts, recommenders, and smart environments. Moreover, the applications reported by the included studies do not allow the execution of actions without the explicit consent of the users, and they integrate personal sensors, sensors deployed in the city and crowdsourcing mechanisms to acquire primary context attributes (i.e., location, activity, and environment) and secondary context attributes (i.e., speed, physical activity, points of interest, stress, and tourists’ emotions). In addition, the included studies present scarce details about context reasoning, data privacy, integrity, and confidentiality, data aggregation and accuracy, and data interoperability. Finally, the results also indicate low maturity level of the developed applications, which is evidenced by the lack of user-centred evaluations, as well as by the difficulty in integrating important technologies being developed for smart cities.

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Section: Demographics Of the Studiesmentioning

confidence: 99%

Section: Characteristics Of the Context-aware Applications Supported ...mentioning

confidence: 99%

Section: Characteristics Of the Context-aware Technologies Being Usedmentioning

confidence: 99%

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Systematic literature review of context-awareness applications supported by smart cities’ infrastructures

et al. 2022

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“…Reference [20] proposed a context-sensitive recommender system and presented a definition of the concept of context through a meta-model. An applied case study was conducted in the city of Tangier.…”

Section: Context-aware Filteringmentioning

confidence: 99%

Hybrid recommender system for tourism based on big data and AI: A conceptual framework

Fararni

Nafis

Aghoutane

et al. 2021

Big Data Min. Anal.

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With the development of the Internet, technology, and means of communication, the production of tourist data has multiplied at all levels (hotels, restaurants, transport, heritage, tourist events, activities, etc.), especially with the development of Online Travel Agency (OTA). However, the list of possibilities offered to tourists by these Web search engines (or even specialized tourist sites) can be overwhelming and relevant results are usually drowned in informational "noise", which prevents, or at least slows down the selection process. To assist tourists in trip planning and help them to find the information they are looking for, many recommender systems have been developed. In this article, we present an overview of the various recommendation approaches used in the field of tourism. From this study, an architecture and a conceptual framework for tourism recommender system are proposed, based on a hybrid recommendation approach. The proposed system goes beyond the recommendation of a list of tourist attractions, tailored to tourist preferences. It can be seen as a trip planner that designs a detailed program, including heterogeneous tourism resources, for a specific visit duration. The ultimate goal is to develop a recommender system based on big data technologies, artificial intelligence, and operational research to promote tourism in Morocco, specifically in the Dara â-Tafilalet region.

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“…For example, functional requirements include resource management, runtime environment, device mobility, service mobility; while nonfunctional requirements include latency, devices heterogeneity, scalability, reliability, context‐awareness, security, interoperability. The above requirements have not been tackled enough in current smart service recommendation solutions, like in References 7–16. In addition, users (e.g., patients, medical staff, tourists, drivers) are often involved in multiple provider information networks simultaneously, to satisfy their diverse and complex needs, which are translated to a combination of smart services.…”

Section: Introductionmentioning

confidence: 99%

Cross‐network service recommendation in smart cities

Mezni,

Sellami,

Al‐Rasheed

et al. 2024

Concurrency and Computation

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SummaryNowadays, Internet of Things, artificial intelligence, cloud computing, and other revolutionary technologies (e.g., edge and fog computing) have become the pillar of smart cities. These latter make users' lives easier, thanks to a wide variety of smart services offered in different dimensions (e.g., smart living, smart mobility, smart economy, smart governance). However, the rapid adoption of smart services by users and the full servicelization of several cities around the world is faced with two major issues: the lack of knowledge regarding smart services' capacities (e.g., features, contextual requirements, pricing models, privacy policies, provisioning terms, etc.), and the lack of unified rating and quantification of smart services' QoS behavior. Indeed, interested users often exploit traditional search tools (e.g., Web search engines, social networks) to find and rate the needed services. This behavior has scattered the smart services' usage data (e.g., users contexts, ratings) across multiple providers platforms, which makes the search task beyond the capacity of users and, even, other service providers. Although recommender systems are a natural solution to exempt users from exploring the huge space of the offered smart services, current recommendation approaches for smart city environments are unable to deliver correct recommendations. In fact, they have been initially designed to single‐network settings (a single service repository), while smart services' consumers often are involved in multiple provider platforms. To the best of our knowledge, there exists no approach that treated smart service recommendation across multiple information networks. Therefore, the goal of this paper is to propose a cross‐network recommender system for smart cities. We first model the multiplex network of smart services' providers as a multirelational fuzzy lattice family thanks to fuzzy relational concept analysis (fuzzy RCA), which is a powerful mathematical method for data analysis and clustering. We also use the concept of anchor users to connect providers networks via the users involved in more than one provider platform. Guided by anchors' cross‐network relations, we compute the similarity between users and we define algorithms for exploring the smart services' information network, i.e. lattice family. Extensive experiments have proved the effectiveness of cross‐network recommendation and the quality of produced recommendations, compared to state‐of‐the‐art single‐network recommendation.

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Toward a Smart Tourism Recommender System: Applied to Tangier City

Cited by 7 publications

References 10 publications

Systematic literature review of context-awareness applications supported by smart cities’ infrastructures

Systematic literature review of context-awareness applications supported by smart cities’ infrastructures

Hybrid recommender system for tourism based on big data and AI: A conceptual framework

Cross‐network service recommendation in smart cities

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